Long memory and structural breaks in commodity futures markets

This study employs daily data for 14 commodities and three financial assets 1990–2009 to explore the impact of the time series properties of the futures‐spot basis and the cost of carry on forward market unbiasedness. The main result is that the basis of 16 assets exhibits both long memory and structural breaks. The long memory in the basis is robust even to the use of break‐adjusted data. It implies that the cost‐of‐carry has long memory which the empirical results confirm using the interest cost as a proxy. These new findings suggest that the forecast error has long memory and are inconsistent with unbiasedness. They could be consistent with a weaker version of market efficiency in the presence of a fractionally integrated, time‐varying risk premium but they could also be rationalized by priced noise trader risk with limits to arbitrage in less than fully efficient markets. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark     

A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity

This paper generalizes existing econometric models for censored competing risks by introducing a new flexible specification based on a piecewise linear baseline hazard, time‐varying regressors, and unobserved individual heterogeneity distributed as an infinite mixture of generalized inverse Gaussian (GIG) densities, nesting the gamma kernel as a special case. A common correlated latent time effect induces dependence among risks. Our model is based on underlying latent exit decisions in continuous time while only a time interval containing the exit time is observed, as is common in economic data. We do not make the simplifying assumption of discretizing exit decisions—our competing risk model setup allows for latent exit times of different risk types to be realized within the same time period. In this setting, we derive a tractable likelihood based on scaled GIG Laplace transforms and their higher‐order derivatives. We apply our approach to analyzing the determinants of unemployment duration with exits to jobs in the same industry or a different industry among unemployment insurance recipients on nationally representative individual‐level survey data from the US Department of Labor. Our approach allows us to conduct a counterfactual policy experiment by changing the replacement rate: we find that the impact of its change on the probability of exit from unemployment is inelastic. Copyright © 2014 John Wiley & Sons, Ltd.     

Numerical issues in threshold autoregressive modeling of time series

This paper analyses the contribution of various numerical approaches to making the estimation of threshold autoregressive time series more efficient. It relies on the computational advantages of QR factorizations and proposes Givens transformations to update these factors for sequential LS problems. By showing that the residual sum of squares is a continuous rational function over threshold intervals it develops a new fitting method based on rational interpolation and the standard necessary optimality condition. Taking as benchmark a simple grid search, the paper illustrates via Monte Carlo simulations the efficiency gains of the proposed tools.     

Comparative Analysis of Thresholds of Non-Revolutionary Institutional Change: China and the United States in the 1980s

Ireland has experienced a series of interlocking banking, fiscal, unemployment, and political crises since 2007. We detail the challenges involved in modeling individual moments of the crisis through the lens of balance sheets and transactions matrices among sectors of the Irish society. We conclude with a series of recommendations for models of small open economies.     

Unilateral effects of mergers with general linear demand

We derive the formula for the unilateral price effects of mergers of two products with linear demand in the general asymmetric situation. The formula uses the same information required to calculate upward pricing pressure in the 2010 Horizontal Merger Guidelines.     

A Poisson mixture model of discrete choice

In this paper, we introduce a new Poisson mixture model for count panel data where the underlying Poisson process intensity is determined endogenously by consumer latent utility maximization over a set of choice alternatives. This formulation accommodates the choice and count in a single random utility framework with desirable theoretical properties. Individual heterogeneity is introduced through a random coefficient scheme with a flexible semiparametric distribution. We deal with the analytical intractability of the resulting mixture by recasting the model as an embedding of infinite sequences of scaled moments of the mixing distribution, and newly derive their cumulant representations along with bounds on their rate of numerical convergence. We further develop an efficient recursive algorithm for fast evaluation of the model likelihood within a Bayesian Gibbs sampling scheme. We apply our model to a recent household panel of supermarket visit counts. We estimate the nonparametric density of three key variables of interest-price, driving distance, and their interaction-while controlling for a range of consumer demographic characteristics. We use this econometric framework to assess the opportunity cost of time and analyze the interaction between store choice, trip frequency, search intensity, and household and store characteristics. We also conduct a counterfactual welfare experiment and compute the compensating variation for a 10%-30% increase in Walmart prices.